Efficient High Dimension Data Clustering using Constraint-Partitioning K-Means Algorithm

Aloysius George
Grian Technologies Private Limited, Research and Development Company, India

 

Abstract: With the ever-increasing size of data, clustering of large dimensional databases poses a demanding task that should satisfy both the requirements of the computation efficiency and result quality. In order to achieve both tasks, clustering of feature space rather than the original data space has received importance among the data mining researchers. Accordingly, we performed data clustering of high dimension dataset using Constraint-Partitioning K-Means (COP-KMEANS) clustering algorithm which did not fit properly to cluster high dimensional data sets in terms of effectiveness and efficiency, because of the intrinsic sparse of high dimensional data and resulted in producing indefinite and inaccurate clusters. Hence, we carry out two steps for clustering high dimension dataset. Initially, we perform dimensionality reduction on the high dimension dataset using Principal Component Analysis (PCA) as a preprocessing step to data clustering. Later, we integrate the COP-KMEANS clustering algorithm to the dimension reduced dataset to produce good and accurate clusters. The performance of the approach is evaluated with high dimensional datasets such as Parkinson’s dataset and Ionosphere dataset. The experimental results showed that the proposed approach is very effective in producing accurate and precise clusters.

Keywords: Clustering, dimensionality reduction, PCA, COP-KMEANS algorithm, clustering accuracy, parkinson's dataset, ionosphere dataset.

 

  Received July 14, 2011; accepted December 30, 2011; published online August 5, 2012

  

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